In [1]:
# Importamos las librerías necesarias
import plotly.graph_objects as go
import pandas as pd

BITCOIN (BTC)¶

In [2]:
BTC = pd.read_csv('../05 - Estudio de técnicas de visualización de datos (PEC2)/BTC.csv')
BTC
Out[2]:
Date Close/Last Volume Open High Low
0 11/23/2022 16695.9 NaN 16501.4 16785.6 16539.9
1 11/22/2022 16508.8 NaN 15817.8 16576.3 16162.3
2 11/21/2022 15836.8 NaN 16147.7 15937.1 15751.7
3 11/20/2022 16711.8 NaN 16638.3 16749.3 16670.4
4 11/18/2022 16633.8 NaN 16691.2 16846.3 16553.0
... ... ... ... ... ... ...
379 11/05/2021 61078.6 NaN 62242.1 61289.5 60828.8
380 11/04/2021 62139.5 NaN 62439.2 62326.3 60856.7
381 11/03/2021 62464.0 NaN 63211.6 63077.6 62293.8
382 11/02/2021 63041.7 NaN 61091.8 63508.6 62810.7
383 11/01/2021 61098.7 NaN 59952.3 61523.4 60651.5

384 rows × 6 columns

In [3]:
# Convertimos la columna Date, de tipo string, a tipo "datetime"
BTC["Date"] = pd.to_datetime(BTC["Date"])
In [4]:
# Comprobamos que es de tipo timestamp
print(type(BTC.Date[0]))

<class 'pandas._libs.tslibs.timestamps.Timestamp'>
In [5]:
# Ordenamos las fechas de más antigua a más reciente
BTC = BTC.sort_values(by='Date', ascending=True)
BTC
Out[5]:
Date Close/Last Volume Open High Low
383 2021-11-01 61098.7 NaN 59952.3 61523.4 60651.5
382 2021-11-02 63041.7 NaN 61091.8 63508.6 62810.7
381 2021-11-03 62464.0 NaN 63211.6 63077.6 62293.8
380 2021-11-04 62139.5 NaN 62439.2 62326.3 60856.7
379 2021-11-05 61078.6 NaN 62242.1 61289.5 60828.8
... ... ... ... ... ... ...
4 2022-11-18 16633.8 NaN 16691.2 16846.3 16553.0
3 2022-11-20 16711.8 NaN 16638.3 16749.3 16670.4
2 2022-11-21 15836.8 NaN 16147.7 15937.1 15751.7
1 2022-11-22 16508.8 NaN 15817.8 16576.3 16162.3
0 2022-11-23 16695.9 NaN 16501.4 16785.6 16539.9

384 rows × 6 columns

In [6]:
fig = go.Figure(data=go.Ohlc(x=BTC['Date'],
                    open=BTC['Open'],
                    high=BTC['High'],
                    low=BTC['Low'],
                    close=BTC['Close/Last']))
fig.show()
In [7]:
# Información principal
fig = go.Figure(data=go.Ohlc(x=BTC['Date'],
                    open=BTC['Open'],
                    high=BTC['High'],
                    low=BTC['Low'],
                    close=BTC['Close/Last']))

# Añadir anotaciones adicionales
fig.update_layout(
    title='Evolución precio Bitcoin (último año)',
    yaxis_title='Precio en dólares ($)',
    shapes = [dict(
        x0='2021-11-08', x1='2021-11-08', y0=0, y1=1, xref='x', yref='paper',
        line_width=2)],
    annotations=[dict(
        x='2021-11-08', y=0.05, xref='x', yref='paper',
        showarrow=False, xanchor='left', text='Bitcoin All Time High (ATH) - 08/11/2021')]
        )

# Mostrar gráfico
fig.show()
In [8]:
fig = go.Figure(data=go.Candlestick(x=BTC['Date'],
                    open=BTC['Open'],
                    high=BTC['High'],
                    low=BTC['Low'],
                    close=BTC['Close/Last']))
fig.show()

ETHEREUM (ETH)¶

In [9]:
ETH = pd.read_csv('../05 - Estudio de técnicas de visualización de datos (PEC2)/ETH.csv')
ETH
Out[9]:
Date Close/Last Volume Open High Low
0 11/26/2022 1207.66 NaN 1199.91 1229.20 1196.93
1 11/25/2022 1220.68 NaN 1175.37 1224.84 1196.93
2 11/24/2022 1174.08 NaN 1203.38 1204.51 1173.97
3 11/23/2022 1203.24 NaN 1163.50 1208.66 1180.91
4 11/22/2022 1163.99 NaN 1101.84 1169.88 1128.53
... ... ... ... ... ... ...
382 11/05/2021 4486.79 NaN 4506.13 4507.00 4456.23
383 11/04/2021 4540.75 NaN 4562.61 4554.23 4470.10
384 11/03/2021 4552.30 NaN 4599.10 4604.84 4541.84
385 11/02/2021 4575.86 NaN 4306.76 4639.60 4556.01
386 11/01/2021 4314.85 NaN 4237.23 4346.39 4287.36

387 rows × 6 columns

In [10]:
# Convertimos la columna Date, de tipo string, a tipo "datetime"
ETH["Date"] = pd.to_datetime(ETH["Date"])

# Ordenamos las fechas de más antigua a más reciente
ETH = ETH.sort_values(by='Date', ascending=True)
In [11]:
# Información principal
fig = go.Figure(data=go.Ohlc(x=ETH['Date'],
                    open=ETH['Open'],
                    high=ETH['High'],
                    low=ETH['Low'],
                    close=ETH['Close/Last']))

# Añadir anotaciones adicionales
fig.update_layout(
    title='Evolución precio Ethereum (NOV 2021 - NOV 2022)',
    yaxis_title='Precio en dólares ($)',
    shapes = [dict(
        x0='2021-11-08', x1='2021-11-08', y0=0, y1=1, xref='x', yref='paper',
        line_width=2)],
    annotations=[dict(
        x='2021-11-08', y=0.05, xref='x', yref='paper',
        showarrow=False, xanchor='left', text='Ethereum All Time High (ATH) - 08/11/2021')]
        )

# Mostrar gráfico
fig.show()

SANTANDER (SAN)¶

In [12]:
SAN = pd.read_csv('../05 - Estudio de técnicas de visualización de datos (PEC2)/SANTANDER.csv')
SAN
Out[12]:
Date Close/Last Volume Open High Low
0 11/25/2022 $2.92 2198617 $2.88 $2.93 $2.88
1 11/23/2022 $2.85 2298938 $2.84 $2.8675 $2.83
2 11/22/2022 $2.85 5516099 $2.8 $2.85 $2.79
3 11/21/2022 $2.75 4780279 $2.69 $2.76 $2.68
4 11/18/2022 $2.68 2017884 $2.68 $2.7 $2.66
... ... ... ... ... ... ...
2514 11/30/2012 $7.3408 4081765 $7.3312 $7.4079 $7.3025
2515 11/29/2012 $7.3025 3788375 $7.3121 $7.3312 $7.0704
2516 11/28/2012 $7.2162 5926391 $7.0246 $7.2354 $6.9767
2517 11/27/2012 $7.0917 2863475 $7.1108 $7.1683 $7.0629
2518 11/26/2012 $7.1396 1671935 $7.13 $7.1683 $7.1108

2519 rows × 6 columns

Hay que eliminar el símbolo del dólar ($) del dataframe, de lo contrario, en el gráfico OHLC, aunque se representarán correctamente las barras de forma gráfica, los valores de apertura y cierre serán nulos:

In [13]:
# Se eliminarán todos los símbolos del dólar del dataframe
SAN = SAN.replace(r'\$', '', regex=True)
SAN
Out[13]:
Date Close/Last Volume Open High Low
0 11/25/2022 2.92 2198617 2.88 2.93 2.88
1 11/23/2022 2.85 2298938 2.84 2.8675 2.83
2 11/22/2022 2.85 5516099 2.8 2.85 2.79
3 11/21/2022 2.75 4780279 2.69 2.76 2.68
4 11/18/2022 2.68 2017884 2.68 2.7 2.66
... ... ... ... ... ... ...
2514 11/30/2012 7.3408 4081765 7.3312 7.4079 7.3025
2515 11/29/2012 7.3025 3788375 7.3121 7.3312 7.0704
2516 11/28/2012 7.2162 5926391 7.0246 7.2354 6.9767
2517 11/27/2012 7.0917 2863475 7.1108 7.1683 7.0629
2518 11/26/2012 7.1396 1671935 7.13 7.1683 7.1108

2519 rows × 6 columns

In [14]:
# Convertimos la columna Date, de tipo string, a tipo "datetime"
SAN["Date"] = pd.to_datetime(SAN["Date"])

# Ordenamos las fechas de más antigua a más reciente
SAN = SAN.sort_values(by='Date', ascending=True)
In [15]:
fig = go.Figure(data=go.Ohlc(x=SAN['Date'],
                    open=SAN['Open'],
                    high=SAN['High'],
                    low=SAN['Low'],
                    close=SAN['Close/Last']))

# Añadir anotaciones adicionales
fig.update_layout(
    title='Fluctuación precio acciones Banco Santander',
    yaxis_title='Precio en dólares ($)',
    shapes = [dict(
        x0='2020-03-01', x1='2020-10-31', y0=0, y1=0.5, xref='x', yref='paper',
        line_width=1)],
    annotations=[dict(
        x='2020-10-31', y=0.55, xref='x', yref='paper',
        showarrow=False, xanchor='right', text='Pandemia COVID-19')]
        )

fig.show()

AENA (AENA)¶

In [16]:
AENA = pd.read_csv('../05 - Estudio de técnicas de visualización de datos (PEC2)/AENA.csv')
AENA
Out[16]:
Date Close/Last Volume Open High Low
0 11/25/2022 $131.5175 15.0 $131.5175 $131.5175 $131.5175
1 11/23/2022 $127.8075 1.0 $127.8075 $127.8075 $127.8075
2 11/22/2022 $126.2725 74.0 $126.2725 $126.2725 $126.2725
3 11/21/2022 $127.625 NaN $127.625 $127.625 $127.625
4 11/18/2022 $127.625 65.0 $127.625 $127.625 $127.625
... ... ... ... ... ... ...
1902 05/08/2015 $98.3 NaN $98.3 $98.3 $98.3
1903 05/07/2015 $98.3 NaN $98.3 $98.3 $98.3
1904 05/06/2015 $98.3 NaN $98.3 $98.3 $98.3
1905 05/05/2015 $98.3 NaN $98.3 $98.3 $98.3
1906 05/04/2015 $98.3 1300.0 $98.3 $98.3 $98.3

1907 rows × 6 columns

In [17]:
# Se eliminarán todos los símbolos del dólar del dataframe
AENA = AENA.replace(r'\$', '', regex=True)

# Convertimos la columna Date, de tipo string, a tipo "datetime"
AENA["Date"] = pd.to_datetime(AENA["Date"])

# Ordenamos las fechas de más antigua a más reciente
AENA = AENA.sort_values(by='Date', ascending=True)
In [18]:
fig = go.Figure(data=go.Ohlc(x=AENA['Date'],
                    open=AENA['Open'],
                    high=AENA['High'],
                    low=AENA['Low'],
                    close=AENA['Close/Last'],
                    increasing_line_color= 'lime', decreasing_line_color= 'violet'))

fig.show()

FUENTES¶

Empresas del IBEX35: https://www.eleconomista.es/indice/IBEX-35/resumen

Descarga de datasets: https://www.nasdaq.com/market-activity/cryptocurrency/eth/historical

Creación gráficos OHLC (Documentación librería plotly): https://plotly.com/python/ohlc-charts/

Análisis de OHLC: https://www.investopedia.com/terms/o/ohlcchart.asp#:~:text=OHLC%20and%20candlestick%20charts%20show,close%20via%20a%20real%20body.

OHLC [VS.] Candlestick: https://financetrain.com/understanding-japanese-candlestick-charts-and-ohlc-charts